Abstract
Deep neural networks (DNNs) have achieved great success in medical image segmentation. However, the DNNs are generally deceived by the adversarial examples, making robustness a key factor of DNNs when applied in the field of medical research. In this paper, in order to evaluate the robustness of medical image segmentation networks, we propose a novel Region-based Dense Adversary Generation (RDAG) method to generate adversarial examples. Specifically, our method attacks the DNNs on both pixel-level and region-of-interesting (ROI) level. The pixel-level attack makes DNNs mistakenly segment each individual pixel. Meanwhile, the ROI-level attack will generate perturbation based on region information. We evaluate our proposed method for medical image segmentation on DRIVE and CELL datasets. The experimental results show that our proposed method achieves effective attack results on both datasets for medical image segmentation when compared with several state-of-the-art methods.
This work was supported by the National Natural Science Foundation of China under Grants 62136004, and 62006115.
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References
Chen, J., Qian, L., Urakov, T., Gu, W., Liang, L.: Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images. In: Medical Imaging 2021: Image Processing, vol. 11596, p. 1159615. International Society for Optics and Photonics (2021)
Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. Adv. Neural Inform. Process. Syst. 34 (2021)
Daza, L., Pérez, J.C., Arbeláez, P.: Towards robust general medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 3–13. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_1
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Drukker, K., Giger, M.L., Horsch, K., Kupinski, M.A., Vyborny, C.J., Mendelson, E.B.: Computerized lesion detection on breast ultrasound. Med. Phys. 29(7), 1438–1446 (2002)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Gu, Z., et al.: CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010)
Li, R., Zhang, W., Suk, H.I., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 305–312. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10443-0_39
Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. arXiv: Learning (2016)
Liu, Z., Zhang, J., Jog, V., Loh, P.L., McMillan, A.B.: Robustifying deep networks for image segmentation. arXiv preprint arXiv:1908.00656 (2019)
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574–2582 (2016)
Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical Imaging 2004: Image Processing, vol. 5370, pp. 648–656. International Society for Optics and Photonics (2004)
Ozbulak, U., Van Messem, A., Neve, W.D.: Impact of adversarial examples on deep learning models for biomedical image segmentation. In: Shen, D., et al (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 300–308. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_34
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Computer and Communications Security (2017)
Paschali, M., Conjeti, S., Navarro, F., Navab, N.: Generalizability vs. robustness: adversarial examples for medical imaging. arXiv preprint arXiv:1804.00504 (2018)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2(1), 315–337 (2000)
Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Tomczak, K., Czerwińska, P., Wiznerowicz, M.: The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19(1A), A68 (2015)
Valverde, J.M., Tohka, J.: Region-wise loss for biomedical image segmentation. arXiv preprint arXiv:2108.01405 (2021)
Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1369–1378 (2017)
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Shen, A., Sun, L., Xu, M., Zhang, D. (2022). Region-Based Dense Adversarial Generation for Medical Image Segmentation. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_9
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